Improved Distributed Predictive Functional Control With Basic Function and PID Control Structure

Yu, Zhe; Bai, Jianjun; Zou, Hongbo

doi:10.1109/access.2020.2967886

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…By following literatures [27,31], the system input U (k) can be structuralized by a linear combination of a group of pre-selected basis functions, which is given by:…”

Section: Input Structuralizationmentioning

confidence: 99%

Design of Model-Free Adaptive Predictive Controllers with Low Online Computational Load for a Class of Discrete-Time Nonlinear Multiple-Input Multiple-Output Systems

Wang

et al. 2022

Preprint

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This paper proposes a novel model-free adaptive predictive control approach with low online computational load for of a class of unknown discretetime nonlinear multiple-input multiple-output systems. First, historical measurable input/output data is utilized to online construct equivalent explicit data models for control design, making the controllers model-free. Second, input structuralization where system inputs are expressed by linear combination of a few basis functions is integrated into predictive controller design for reducing online computational load. Third, a novel Kalman Filter-based regression factor computation approach is developed to predict data model parameters for realizing predictive function of controllers such that closed-loop system robustness can be enhanced when sudden changes from prescribed references are encountered. Last, numerical simulations validate that, by using much less online computational load, the proposed approach can achieve equivalent control performance compared with the existing approaches.

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“…By following literatures [27,31], the system input U (k) can be structuralized by a linear combination of a group of pre-selected basis functions, which is given by:…”

Section: Input Structuralizationmentioning

confidence: 99%

Design of Model-Free Adaptive Predictive Controllers with Low Online Computational Load for a Class of Discrete-Time Nonlinear Multiple-Input Multiple-Output Systems

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Distributed dynamic matrix control, as a typical representative of DMPC uses the theory and thought of distributed control for reference and decomposes complicated large-scale system into multiple subsystems. Each subsystem is controlled through an independent dynamic matrix controller, and each controller transmits information through network interconnection, and based on the idea of Nash optimization, control optimization of the whole large-scale system can be realized [30][31]. In [32], for fractional order system, Wang et al designed a distributed PID type dynamic matrix control (PID-DDMC) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Design of Distributed Fractional Order PID Type Dynamic Matrix Controller for Large-Scale Process Systems

Teng

2020

IEEE Access

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As a typical representative of distributed model predictive control, distributed dynamic matrix control (DDMC) is able to satisfy the basic control requirements for large-scale systems. However, the constraints and disturbances in actual industrial process usually lead to the slow set-point target tracking, large overshoot and weak anti-interference ability of the system. Therefore, the relevant requirements may not be met for some complex industrial processes. The existing distributed PID type dynamic matrix control (PID-DDMC) method can improve the control performance, but it maybe not accurate enough in some cases. Based on this background, this paper introduces fractional order PID (FOPID) into distributed dynamic matrix control, and proposes a distributed fractional order PID type dynamic matrix control (FOPID-DDMC) algorithm. To compare with the conventional PID control, it expands the control and parameter setting range of the controller, and makes the control effect of the controller more accurate. Furthermore, the coupling effect among subsystems is dispelled by adopting the Nash optimal theory, and information interaction between the subsystems through network communication is realized, thereby, completing the optimization of the whole large-scale system. Finally, through a numerical simulation example and a level-temperature control process, the feasibility of the proposed algorithm is demonstrated by comparing with the traditional DDMC and PID-DDMC.

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General regression neural network-based data-driven model-free predictive functional control for a class of discrete-time nonlinear systems

Wang

Zhang

2021

Nonlinear Dyn

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Improved Distributed Predictive Functional Control With Basic Function and PID Control Structure

Cited by 5 publications

References 41 publications

Design of Model-Free Adaptive Predictive Controllers with Low Online Computational Load for a Class of Discrete-Time Nonlinear Multiple-Input Multiple-Output Systems

Design of Model-Free Adaptive Predictive Controllers with Low Online Computational Load for a Class of Discrete-Time Nonlinear Multiple-Input Multiple-Output Systems

Design of Distributed Fractional Order PID Type Dynamic Matrix Controller for Large-Scale Process Systems

General regression neural network-based data-driven model-free predictive functional control for a class of discrete-time nonlinear systems

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